Source code for transformers.modeling_tf_utils

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""TF general model utils."""

import functools
import inspect
import os
import pickle
import re
import warnings
from typing import Dict, List, Optional, Union

import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.engine import data_adapter
from tensorflow.python.keras.engine.keras_tensor import KerasTensor
from tensorflow.python.keras.saving import hdf5_format

from huggingface_hub import Repository, list_repo_files

from .configuration_utils import PretrainedConfig
from .file_utils import (
    DUMMY_INPUTS,
    TF2_WEIGHTS_NAME,
    WEIGHTS_NAME,
    ModelOutput,
    PushToHubMixin,
    cached_path,
    copy_func,
    hf_bucket_url,
    is_offline_mode,
    is_remote_url,
)
from .generation_tf_utils import TFGenerationMixin
from .modeling_tf_outputs import TFSeq2SeqLMOutput
from .tokenization_utils_base import BatchEncoding
from .utils import logging


logger = logging.get_logger(__name__)
tf_logger = tf.get_logger()

TFModelInputType = Union[
    List[tf.Tensor],
    List[np.ndarray],
    List[KerasTensor],
    Dict[str, tf.Tensor],
    Dict[str, np.ndarray],
    Dict[str, KerasTensor],
    tf.Tensor,
    np.ndarray,
    KerasTensor,
]


def dummy_loss(y_true, y_pred):
    return tf.reduce_mean(y_pred)


[docs]class TFModelUtilsMixin: """ A few utilities for :obj:`tf.keras.Model`, to be used as a mixin. """
[docs] def num_parameters(self, only_trainable: bool = False) -> int: """ Get the number of (optionally, trainable) parameters in the model. Args: only_trainable (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to return only the number of trainable parameters Returns: :obj:`int`: The number of parameters. """ if only_trainable: return int(sum(np.prod(w.shape.as_list()) for w in self.trainable_variables)) else: return self.count_params()
[docs]def keras_serializable(cls): """ Decorate a Keras Layer class to support Keras serialization. This is done by: 1. Adding a :obj:`transformers_config` dict to the Keras config dictionary in :obj:`get_config` (called by Keras at serialization time. 2. Wrapping :obj:`__init__` to accept that :obj:`transformers_config` dict (passed by Keras at deserialization time) and convert it to a config object for the actual layer initializer. 3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not need to be supplied in :obj:`custom_objects` in the call to :obj:`tf.keras.models.load_model`. Args: cls (a :obj:`tf.keras.layers.Layers subclass`): Typically a :obj:`TF.MainLayer` class in this project, in general must accept a :obj:`config` argument to its initializer. Returns: The same class object, with modifications for Keras deserialization. """ initializer = cls.__init__ config_class = getattr(cls, "config_class", None) if config_class is None: raise AttributeError("Must set `config_class` to use @keras_serializable") @functools.wraps(initializer) def wrapped_init(self, *args, **kwargs): config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.pop("config", None) if isinstance(config, dict): config = config_class.from_dict(config) initializer(self, config, *args, **kwargs) elif isinstance(config, PretrainedConfig): if len(args) > 0: initializer(self, *args, **kwargs) else: initializer(self, config, *args, **kwargs) else: raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)") self._config = config self._kwargs = kwargs cls.__init__ = wrapped_init if not hasattr(cls, "get_config"): raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses") if hasattr(cls.get_config, "_is_default"): def get_config(self): cfg = super(cls, self).get_config() cfg["config"] = self._config.to_dict() cfg.update(self._kwargs) return cfg cls.get_config = get_config cls._keras_serializable = True if hasattr(tf.keras.utils, "register_keras_serializable"): cls = tf.keras.utils.register_keras_serializable()(cls) return cls
[docs]class TFCausalLanguageModelingLoss: """ Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. """ def compute_loss(self, labels, logits): loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) # make sure only labels that are not equal to -100 affect the loss active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100) reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss) labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss) return loss_fn(labels, reduced_logits)
[docs]class TFQuestionAnsweringLoss: """ Loss function suitable for question answering. """ def compute_loss(self, labels, logits): loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) start_loss = loss_fn(labels["start_position"], logits[0]) end_loss = loss_fn(labels["end_position"], logits[1]) return (start_loss + end_loss) / 2.0
[docs]class TFTokenClassificationLoss: """ Loss function suitable for token classification. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. """ def compute_loss(self, labels, logits): loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) # make sure only labels that are not equal to -100 # are taken into account as loss if tf.math.reduce_any(labels == -1): warnings.warn("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.") active_loss = tf.reshape(labels, (-1,)) != -1 else: active_loss = tf.reshape(labels, (-1,)) != -100 reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss) labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss) return loss_fn(labels, reduced_logits)
[docs]class TFSequenceClassificationLoss: """ Loss function suitable for sequence classification. """ def compute_loss(self, labels, logits): if len(shape_list(logits)) == 1 or shape_list(logits)[1] == 1: loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) else: loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) return loss_fn(labels, logits)
[docs]class TFMultipleChoiceLoss: """Loss function suitable for multiple choice tasks.""" def compute_loss(self, labels, logits): loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) return loss_fn(labels, logits)
[docs]class TFMaskedLanguageModelingLoss(TFCausalLanguageModelingLoss): """ Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. """
class TFNextSentencePredictionLoss: """ Loss function suitable for next sentence prediction (NSP), that is, the task of guessing the next sentence. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. """ def compute_loss(self, labels, logits): loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE ) # make sure only labels that are not equal to -100 # are taken into account as loss next_sentence_active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100) next_sentence_reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, 2)), next_sentence_active_loss) next_sentence_label = tf.boolean_mask(tf.reshape(labels, (-1,)), next_sentence_active_loss) return loss_fn(next_sentence_label, next_sentence_reduced_logits) def booleans_processing(config, **kwargs): """ Process the input booleans of each model in order to be sure they are compliant with the execution mode (eager or graph) Args: config (:class:`~transformers.PretrainedConfig`): The config of the running model. **kwargs: The boolean parameters Returns: A dictionary with the proper values for each boolean """ final_booleans = {} if tf.executing_eagerly(): final_booleans["output_attentions"] = ( kwargs["output_attentions"] if kwargs["output_attentions"] is not None else config.output_attentions ) final_booleans["output_hidden_states"] = ( kwargs["output_hidden_states"] if kwargs["output_hidden_states"] is not None else config.output_hidden_states ) final_booleans["return_dict"] = ( kwargs["return_dict"] if kwargs["return_dict"] is not None else config.return_dict ) if "use_cache" in kwargs: final_booleans["use_cache"] = ( kwargs["use_cache"] if kwargs["use_cache"] is not None else getattr(config, "use_cache", None) ) else: if ( kwargs["output_attentions"] not in (None, config.output_attentions) or kwargs["output_hidden_states"] not in (None, config.output_hidden_states) or ("use_cache" in kwargs and kwargs["use_cache"] not in (None, config.use_cache)) ): tf_logger.warning( "The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model. " "They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`)." ) final_booleans["output_attentions"] = config.output_attentions final_booleans["output_hidden_states"] = config.output_hidden_states if kwargs.get("return_dict", None) not in (None, True): tf_logger.warning( "The parameter `return_dict` cannot be set in graph mode and will always be set to `True`." ) final_booleans["return_dict"] = True if "use_cache" in kwargs: final_booleans["use_cache"] = getattr(config, "use_cache", None) return final_booleans def input_processing(func, config, input_ids, **kwargs): """ Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input has to be named accordingly to the parameters name, i.e. `input_ids = tf.keras.Input(shape=(128,), dtype='int32', name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training. Args: func (:obj:`callable`): The callable function of the TensorFlow model. config (:class:`~transformers.PretrainedConfig`): The config of the running model. **kwargs: The inputs of the model. Returns: Two lists, one for the missing layers, and another one for the unexpected layers. """ signature = dict(inspect.signature(func).parameters) signature.pop("kwargs", None) signature.pop("self", None) parameter_names = list(signature.keys()) output = {} allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray, KerasTensor) if "inputs" in kwargs["kwargs_call"]: warnings.warn( "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.", FutureWarning, ) output["input_ids"] = kwargs["kwargs_call"].pop("inputs") if "decoder_cached_states" in kwargs["kwargs_call"]: warnings.warn( "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) output["past_key_values"] = kwargs["kwargs_call"].pop("decoder_cached_states") if "past" in kwargs["kwargs_call"] and "past_key_values" in kwargs: warnings.warn( "The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) kwargs["past_key_values"] = kwargs["kwargs_call"].pop("past") elif "past_key_values" in kwargs["kwargs_call"] and "past" in kwargs: kwargs["past"] = kwargs["kwargs_call"].pop("past_key_values") if len(kwargs["kwargs_call"]) > 0: raise ValueError( f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}." ) kwargs.pop("kwargs_call") for k, v in kwargs.items(): if isinstance(v, allowed_types) or v is None: output[k] = v else: raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.") if isinstance(input_ids, (tuple, list)): for i, input in enumerate(input_ids): # EagerTensors don't allow to use the .name property so we check for a real Tensor if type(input) == tf.Tensor: # Tensor names have always the pattern `name:id` then we check only the # `name` part tensor_name = input.name.split(":")[0] if tensor_name in parameter_names: output[tensor_name] = input else: output[parameter_names[i]] = input elif isinstance(input, allowed_types) or input is None: output[parameter_names[i]] = input else: raise ValueError( f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for {parameter_names[i]}." ) elif isinstance(input_ids, (dict, BatchEncoding)): if "inputs" in input_ids: warnings.warn( "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.", FutureWarning, ) output["input_ids"] = input_ids.pop("inputs") if "decoder_cached_states" in input_ids: warnings.warn( "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) output["past_key_values"] = input_ids.pop("decoder_cached_states") for k, v in dict(input_ids).items(): if isinstance(v, allowed_types) or v is None: output[k] = v elif k not in parameter_names and "args" not in parameter_names: logger.warning( f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored." ) continue else: raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.") else: if isinstance(input_ids, (tf.Tensor, KerasTensor)) or input_ids is None: output[parameter_names[0]] = input_ids else: raise ValueError( f"Data of type {type(input_ids)} is not allowed only {allowed_types} is accepted for {parameter_names[0]}." ) for name in parameter_names: if name not in list(output.keys()) and name != "args": output[name] = kwargs.pop(name, signature[name].default) # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs) # So to respect the proper output we have to add this exception if "args" in output: if output["args"] is not None and type(output["args"]) == tf.Tensor: tensor_name = output["args"].name.split(":")[0] output[tensor_name] = output["args"] else: # `args` in this case is always the first parameter, then `input_ids` output["input_ids"] = output["args"] del output["args"] if "kwargs" in output: del output["kwargs"] boolean_dict = { k: v for k, v in output.items() if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"] } output.update( booleans_processing( config=config, **boolean_dict, ) ) return output def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None): """ Detect missing and unexpected layers and load the TF weights accordingly to their names and shapes. Args: model (:obj:`tf.keras.models.Model`): The model to load the weights into. resolved_archive_file (:obj:`str`): The location of the H5 file. ignore_mismatched_sizes (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to ignore weights with shapes that don't match between the checkpoint of the model. Returns: Three lists, one for the missing layers, another one for the unexpected layers, and a last one for the mismatched layers. """ missing_layers = [] unexpected_layers = [] mismatched_layers = [] # Read the H5 file with h5py.File(resolved_archive_file, "r") as f: # Retrieve the name of each layer from the H5 file saved_h5_model_layers_name = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names")) # Find the missing layers from the high level list of layers missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name) # Find the unexpected layers from the high level list of layers unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers])) saved_weight_names_set = set() symbolic_weights_names = set() weight_value_tuples = [] # Compute missing and unexpected sub layers # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...] for layer in model.layers: # if layer_name from the H5 file belongs to the layers from the instantiated model if layer.name in saved_h5_model_layers_name: # Get the H5 layer object from its name h5_layer_object = f[layer.name] # Get all the weights as a list from the layer object symbolic_weights = layer.trainable_weights + layer.non_trainable_weights saved_weights = {} # Create a dict from the H5 saved model that looks like {"weight_name": weight_value} # And a set with only the names for weight_name in hdf5_format.load_attributes_from_hdf5_group(h5_layer_object, "weight_names"): # TF names always start with the model name so we ignore it name = "/".join(weight_name.split("/")[1:]) if _prefix is not None: name = _prefix + "/" + name saved_weights[name] = np.asarray(h5_layer_object[weight_name]) # Add the updated name to the final list for computing missing/unexpected values saved_weight_names_set.add(name) # Loop over each weights from the instantiated model and compare with the weights from the H5 file for symbolic_weight in symbolic_weights: # TF names always start with the model name so we ignore it if _prefix is not None: delimeter = len(_prefix.split("/")) symbolic_weight_name = "/".join( symbolic_weight.name.split("/")[:delimeter] + symbolic_weight.name.split("/")[delimeter + 1 :] ) else: symbolic_weight_name = "/".join(symbolic_weight.name.split("/")[1:]) # here we check if the current weight is among the weights from the H5 file # If yes, get the weight_value of the corresponding weight from the H5 file # If not, make the value to None saved_weight_value = saved_weights.get(symbolic_weight_name, None) # Add the updated name to the final list for computing missing/unexpected values symbolic_weights_names.add(symbolic_weight_name) # If the current weight is found if saved_weight_value is not None: # Check if the shape of the current weight and the one from the H5 file are different if K.int_shape(symbolic_weight) != saved_weight_value.shape: # If yes we reshape the weight from the H5 file accordingly to the current weight # If the two shapes are not compatible we raise an issue try: array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight)) except ValueError as e: if ignore_mismatched_sizes: mismatched_layers.append( (symbolic_weight_name, saved_weight_value.shape, K.int_shape(symbolic_weight)) ) continue else: raise e else: array = saved_weight_value # We create the tuple that will be loaded and add it to the final list weight_value_tuples.append((symbolic_weight, array)) # Load all the weights K.batch_set_value(weight_value_tuples) # Compute the missing and unexpected layers missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set)) unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names)) return missing_layers, unexpected_layers, mismatched_layers def init_copy_embeddings(old_embeddings, new_num_tokens): r""" This function aims to reduce the embeddings in case new_num_tokens < old_num_tokens or to pad with -1 in case new_num_tokens > old_num_tokens. A mask is also computed in order to know which weight in the embeddings should be kept or not. Example: - if new_num_tokens=5 and old_num_tokens=4 and old_embeddings=[w1,w2,w3,w4] - mask=[True,True,True,True,False] and current_weights=[w1,w2,w3,w4,-1] - if new_num_tokens=4 and old_num_tokens=5 and old_embeddings=[w1,w2,w3,w4,w5] - mask=[True,True,True,True] and current_weights=[w1,w2,w3,w4] """ old_num_tokens, old_embedding_dim = shape_list(old_embeddings) size_diff = new_num_tokens - old_num_tokens # initialize new embeddings # Copy token embeddings from the previous ones if tf.math.greater(size_diff, 0): # if the new size is greater than the old one, we extend the current embeddings with a padding until getting new size # and we create a mask to properly identify the padded values and be replaced by the values of the newly created # embeddings current_weights = tf.pad( old_embeddings.value(), tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=-1 ) num_tokens_to_copy = min(old_num_tokens, new_num_tokens) mask = tf.fill(tf.convert_to_tensor([num_tokens_to_copy, 1]), True) mask = tf.pad(mask, tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=False) else: # if the new size if lower than the old one, we take the current embeddings until the new size current_weights = tf.slice( old_embeddings.value(), tf.convert_to_tensor([0, 0]), tf.convert_to_tensor([new_num_tokens, old_embedding_dim]), ) mask = tf.fill(tf.convert_to_tensor([new_num_tokens, 1]), True) return mask, current_weights
[docs]class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushToHubMixin): r""" Base class for all TF models. :class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading, downloading and saving models as well as a few methods common to all models to: * resize the input embeddings, * prune heads in the self-attention heads. Class attributes (overridden by derived classes): - **config_class** (:class:`~transformers.PretrainedConfig`) -- A subclass of :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture. - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model. """ config_class = None base_model_prefix = "" # a list of re pattern of tensor names to ignore from the model when loading the model weights # (and avoid unnecessary warnings). _keys_to_ignore_on_load_missing = None # a list of re pattern of tensor names to ignore from the weights when loading the model weights # (and avoid unnecessary warnings). _keys_to_ignore_on_load_unexpected = None _requires_load_weight_prefix = False @property def dummy_inputs(self) -> Dict[str, tf.Tensor]: """ Dummy inputs to build the network. Returns: :obj:`Dict[str, tf.Tensor]`: The dummy inputs. """ return { "input_ids": tf.constant(DUMMY_INPUTS), } @property def framework(self) -> str: """ :str: Identifies that this is a TensorFlow model. """ return "tf" def __init__(self, config, *inputs, **kwargs): super().__init__(*inputs, **kwargs) if not isinstance(config, PretrainedConfig): raise ValueError( f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class " "`PretrainedConfig`. To create a model from a pretrained model use " f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`" ) # Save config and origin of the pretrained weights if given in model self.config = config self.name_or_path = config.name_or_path
[docs] def get_config(self): return self.config.to_dict()
[docs] @classmethod def from_config(cls, config, **kwargs): if isinstance(config, PretrainedConfig): return cls._from_config(config, **kwargs) return cls._from_config(cls.config_class.from_dict(config, **kwargs))
@classmethod def _from_config(cls, config, **kwargs): """ All context managers that the model should be initialized under go here. """ return cls(config, **kwargs)
[docs] @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"), "token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"), } ] ) def serving(self, inputs): """ Method used for serving the model. Args: inputs (:obj:`Dict[str, tf.Tensor]`): The input of the saved model as a dictionary of tensors. """ output = self.call(inputs) return self.serving_output(output)
[docs] def serving_output(output): """ Prepare the output of the saved model. Each model must implement this function. Args: output (:obj:`~transformers.TFBaseModelOutput`): The output returned by the model. """ raise NotImplementedError
[docs] def get_input_embeddings(self) -> tf.keras.layers.Layer: """ Returns the model's input embeddings layer. Returns: :obj:`tf.Variable`: The embeddings layer mapping vocabulary to hidden states. """ main_layer = getattr(self, self.base_model_prefix, self) if main_layer is not self: return main_layer.get_input_embeddings() else: raise NotImplementedError
def _save_checkpoint(self, checkpoint_dir, epoch): if not os.path.isdir(checkpoint_dir): os.mkdir(checkpoint_dir) # We avoid tf.train.checkpoint or saving weights in TF format, even though that includes optimizer # state for us, because it requires special handling for objects like custom losses, which we use # internally and which users are likely to use too weights_path = os.path.join(checkpoint_dir, "weights.h5") self.save_weights(weights_path) extra_data = {"epoch": epoch, "optimizer_state": self.optimizer.get_weights()} extra_data_path = os.path.join(checkpoint_dir, "extra_data.pickle") with open(extra_data_path, "wb") as f: pickle.dump(extra_data, f)
[docs] def load_repo_checkpoint(self, repo_path_or_name): """ Loads a saved checkpoint (model weights and optimizer state) from a repo. Returns the current epoch count when the checkpoint was made. Args: repo_path_or_name (:obj:`str`): Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case the repository will have the name of that local folder). Returns: :obj:`dict`: A dictionary of extra metadata from the checkpoint, most commonly an "epoch" count. """ if getattr(self, "optimizer", None) is None: raise RuntimeError( "Checkpoint loading failed as no optimizer is attached to the model. " "This is most likely caused by the model not being compiled." ) if not os.path.isdir(repo_path_or_name): # If this isn't a local path, check that the remote repo exists and has a checkpoint in it repo_files = list_repo_files(repo_path_or_name) for file in ("checkpoint/weights.h5", "checkpoint/extra_data.pickle"): if file not in repo_files: raise FileNotFoundError(f"Repo {repo_path_or_name} does not contain checkpoint file {file}!") if "/" not in repo_path_or_name: model_id = repo_path_or_name repo_path_or_name = self.get_full_repo_name(repo_path_or_name) else: model_id = repo_path_or_name.split("/")[-1] repo = Repository(model_id, clone_from=f"https://huggingface.co/{repo_path_or_name}") local_dir = repo.local_dir else: local_dir = repo_path_or_name # Now make sure the repo actually has a checkpoint in it. checkpoint_dir = os.path.join(local_dir, "checkpoint") weights_file = os.path.join(checkpoint_dir, "weights.h5") if not os.path.isfile(weights_file): raise FileNotFoundError(f"Could not find checkpoint file weights.h5 in repo {repo_path_or_name}!") extra_data_file = os.path.join(checkpoint_dir, "extra_data.pickle") if not os.path.isfile(extra_data_file): raise FileNotFoundError(f"Could not find checkpoint file extra_data.pickle in repo {repo_path_or_name}!") # Assuming the repo is real and we got a checkpoint, load the weights and the optimizer state into the model. # The optimizer state includes the iteration count, so learning rate schedules should resume as normal too. self.load_weights(weights_file) with open(extra_data_file, "rb") as f: extra_data = pickle.load(f) self.optimizer.set_weights(extra_data["optimizer_state"]) # Finally, return the epoch number from the checkpoint. This isn't a property of the model, so we can't # set it directly, but the user can pass it to fit(). return {"epoch": extra_data["epoch"]}
[docs] def compile( self, optimizer="rmsprop", loss="passthrough", metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, **kwargs ): """ This is a thin wrapper that sets the model's loss output head as the loss if the user does not specify a loss function themselves. """ if loss == "passthrough": logger.warning( "No loss specified in compile() - the model's internal loss computation will be used as the " "loss. Don't panic - this is a common way to train TensorFlow models in Transformers! " "Please ensure your labels are passed as the 'labels' key of the input dict so that they are " "accessible to the model during the forward pass. To disable this behaviour, please pass a " "loss argument, or explicitly pass loss=None if you do not want your model to compute a loss." ) loss = {"loss": dummy_loss} super().compile( optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, steps_per_execution=steps_per_execution, **kwargs, )
[docs] def train_step(self, data): """ A modification of Keras's default train_step that cleans up the printed metrics when we use a dummy loss. """ # These are the only transformations `Model.fit` applies to user-input # data when a `tf.data.Dataset` is provided. data = data_adapter.expand_1d(data) x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data) # These next two lines differ from the base method - they avoid issues when the labels are in # the input dict (and loss is computed internally) if y is None and "labels" in x: y = x["labels"] # Stops confusion with metric computations # Run forward pass. with tf.GradientTape() as tape: y_pred = self(x, training=True) loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses) # Run backwards pass. self.optimizer.minimize(loss, self.trainable_variables, tape=tape) # When y_pred is a ModelOutput and y is a tf.Tensor the metrics update # should be done only with the relevant ModelOutput param that is # considered by the loss. if isinstance(y_pred, TFSeq2SeqLMOutput) and isinstance(y, tf.Tensor): y_pred = y_pred["logits"] self.compiled_metrics.update_state(y, y_pred, sample_weight) # Collect metrics to return return_metrics = {} for metric in self.metrics: result = metric.result() if isinstance(result, dict): return_metrics.update(result) else: return_metrics[metric.name] = result # These next two lines are also not in the base method - they correct the displayed metrics # when we're using a dummy loss, to avoid a bogus "loss_loss" value being shown. if "loss" in return_metrics and "loss_loss" in return_metrics: del return_metrics["loss_loss"] return return_metrics
[docs] def test_step(self, data): """ A modification of Keras's default test_step that cleans up the printed metrics when we use a dummy loss. """ data = data_adapter.expand_1d(data) x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data) # These next two lines differ from the base method - they avoid issues when the labels are in # the input dict (and loss is computed internally) if y is None and "labels" in x: y = x["labels"] # Stops confusion with metric computations y_pred = self(x, training=False) self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses) # Updates stateful loss metrics. if isinstance(y_pred, TFSeq2SeqLMOutput) and isinstance(y, tf.Tensor): y_pred = y_pred["logits"] self.compiled_metrics.update_state(y, y_pred, sample_weight) # Collect metrics to return return_metrics = {} for metric in self.metrics: result = metric.result() if isinstance(result, dict): return_metrics.update(result) else: return_metrics[metric.name] = result # These next two lines are also not in the base method - they correct the displayed metrics # when we're using a dummy loss, to avoid a bogus "loss_loss" value being shown. if "loss" in return_metrics and "loss_loss" in return_metrics: del return_metrics["loss_loss"] return return_metrics
[docs] def set_input_embeddings(self, value): """ Set model's input embeddings Args: value (:obj:`tf.Variable`): The new weights mapping hidden states to vocabulary. """ main_layer = getattr(self, self.base_model_prefix) if main_layer is None: raise NotImplementedError("The model does not implements the base_model_prefix attribute.") try: main_layer.set_input_embeddings(value) except AttributeError: logger.info("Building the model") self(self.dummy_inputs) main_layer.set_input_embeddings(value)
[docs] def get_output_embeddings(self) -> Union[None, tf.keras.layers.Layer]: """ Returns the model's output embeddings Returns: :obj:`tf.Variable`: The new weights mapping vocabulary to hidden states. """ if self.get_lm_head() is not None: lm_head = self.get_lm_head() try: return lm_head.get_output_embeddings() except AttributeError: logger.info("Building the model") self(self.dummy_inputs) return lm_head().get_output_embeddings() return None # Overwrite for models with output embeddings
[docs] def set_output_embeddings(self, value): """ Set model's output embeddings Args: value (:obj:`tf.Variable`): The new weights mapping hidden states to vocabulary. """ if self.get_lm_head() is not None: lm_head = self.get_lm_head() try: lm_head.set_output_embeddings(value) except AttributeError: logger.info("Building the model") self(self.dummy_inputs) lm_head.set_output_embeddings(value)
[docs] def get_output_layer_with_bias(self) -> Union[None, tf.keras.layers.Layer]: """ Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the embeddings Return: :obj:`tf.keras.layers.Layer`: The layer that handles the bias, None if not an LM model. """ warnings.warn( "The method get_output_layer_with_bias is deprecated. Please use `get_lm_head` instead.", FutureWarning ) return self.get_lm_head()
[docs] def get_prefix_bias_name(self) -> Union[None, str]: """ Get the concatenated _prefix name of the bias from the model name to the parent layer Return: :obj:`str`: The _prefix name of the bias. """ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return None
[docs] def get_bias(self) -> Union[None, Dict[str, tf.Variable]]: """ Dict of bias attached to an LM head. The key represents the name of the bias attribute. Return: :obj:`tf.Variable`: The weights representing the bias, None if not an LM model. """ if self.get_lm_head() is not None: lm_head = self.get_lm_head() try: return lm_head.get_bias() except AttributeError: self(self.dummy_inputs) return lm_head.get_bias() return None
[docs] def set_bias(self, value): """ Set all the bias in the LM head. Args: value (:obj:`Dict[tf.Variable]`): All the new bias attached to an LM head. """ if self.get_lm_head() is not None: lm_head = self.get_lm_head() try: lm_head.set_bias(value) except AttributeError: self(self.dummy_inputs) lm_head.set_bias(value)
[docs] def get_lm_head(self) -> tf.keras.layers.Layer: """ The LM Head layer. This method must be overwritten by all the models that have a lm head. Return: :obj:`tf.keras.layers.Layer`: The LM head layer if the model has one, None if not. """ return None
[docs] def resize_token_embeddings(self, new_num_tokens=None) -> tf.Variable: """ Resizes input token embeddings matrix of the model if :obj:`new_num_tokens != config.vocab_size`. Takes care of tying weights embeddings afterwards if the model class has a :obj:`tie_weights()` method. Arguments: new_num_tokens (:obj:`int`, `optional`): The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens :obj:`tf.Variable` module of the model without doing anything. Return: :obj:`tf.Variable`: Pointer to the input tokens Embeddings Module of the model. """ if new_num_tokens is None or new_num_tokens == self.config.vocab_size: return self._get_word_embedding_weight(self.get_input_embeddings()) model_embeds = self._resize_token_embeddings(new_num_tokens) # Update base model and current model config self.config.vocab_size = new_num_tokens return model_embeds
def _get_word_embedding_weight(model, embedding_layer): embeds = getattr(embedding_layer, "weight", None) if embeds is not None: return embeds embeds = getattr(embedding_layer, "decoder", None) if embeds is not None: return embeds # The reason why the attributes don't exist might be # because the model is not built, so retry getting # the argument after building the model model(model.dummy_inputs) embeds = getattr(embedding_layer, "weight", None) if embeds is not None: return embeds embeds = getattr(embedding_layer, "decoder", None) if embeds is not None: return embeds return None def _resize_token_embeddings(self, new_num_tokens): old_embeddings = self._get_word_embedding_weight(self.get_input_embeddings()) new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) # if word embeddings are not tied, make sure that lm head bias is resized as well if self.get_bias() is not None: old_lm_head_bias = self.get_bias() new_lm_head_bias = self._get_resized_lm_head_bias(old_lm_head_bias, new_num_tokens) self.set_bias(new_lm_head_bias) # if word embeddings are not tied, make sure that lm head decoder is resized as well if self.get_output_embeddings() is not None: old_lm_head_decoder = self._get_word_embedding_weight(self.get_output_embeddings()) new_lm_head_decoder = self._get_resized_lm_head_decoder(old_lm_head_decoder, new_num_tokens) self.set_output_embeddings(new_lm_head_decoder) self.set_input_embeddings(new_embeddings) return self.get_input_embeddings() def _get_resized_lm_head_bias(self, old_lm_head_bias, new_num_tokens): """ Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end Args: old_lm_head_bias (:obj:`tf.Variable`): Old lm head bias to be resized. new_num_tokens (:obj:`int`, `optional`): New number of tokens in the linear matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`, just returns None Return: :obj:`tf.Variable`: Pointer to the resized bias. """ new_lm_head_bias = {} for attr, weight in old_lm_head_bias.items(): first_dim, old_num_tokens = (None, shape_list(weight)[0]) if tf.rank(weight) == 1 else shape_list(weight) size_diff = new_num_tokens - old_num_tokens final_shape = [new_num_tokens] if first_dim is None else [first_dim, new_num_tokens] # initialize new bias if tf.math.greater(size_diff, 0): padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]] current_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape), constant_values=-1) num_tokens_to_copy = min(old_num_tokens, new_num_tokens) mask_shape = [num_tokens_to_copy] if first_dim is None else [1, num_tokens_to_copy] bias_mask = tf.fill(tf.convert_to_tensor(mask_shape), True) bias_mask = tf.pad(bias_mask, tf.convert_to_tensor(padding_shape), constant_values=False) else: slice_from = [0] if first_dim is None else [0, 0] current_bias = tf.slice( weight.value(), tf.convert_to_tensor(slice_from), tf.convert_to_tensor(final_shape) ) bias_mask = tf.fill(tf.convert_to_tensor(final_shape), True) new_bias = self.add_weight( shape=final_shape, initializer="zeros", trainable=True, name=weight.name.split(":")[0], ) init_bias = tf.where(bias_mask, current_bias, new_bias.value()) new_bias.assign(init_bias) new_lm_head_bias[attr] = new_bias return new_lm_head_bias def _get_resized_lm_head_decoder(self, old_lm_head_decoder, new_num_tokens): """ Build a resized decoder from the old ones. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end Args: old_lm_head_decoder (:obj:`tf.Variable`): Old lm head decoder to be resized. new_num_tokens (:obj:`int`, `optional`): New number of tokens in the linear matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`, just returns None Return: :obj:`tf.Variable`: Pointer to the resized decoder or None if the output embeddings are different from the input ones. """ new_lm_head_decoder = old_lm_head_decoder is_input_output_equals = tf.reduce_any( self._get_word_embedding_weight(self.get_input_embeddings()) == old_lm_head_decoder ) if old_lm_head_decoder is not None and not is_input_output_equals: old_embedding_dim = shape_list(old_lm_head_decoder)[1] decoder_mask, current_decoder = init_copy_embeddings(old_lm_head_decoder, new_num_tokens) new_lm_head_decoder = self.add_weight( shape=(new_num_tokens, old_embedding_dim), initializer="zeros", trainable=True, name=old_lm_head_decoder.name.split(":")[0], ) init_decoder = tf.where(decoder_mask, current_decoder, new_lm_head_decoder.value()) new_lm_head_decoder.assign(init_decoder) return new_lm_head_decoder def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None) -> tf.Variable: """ Build a resized Embedding weights from a provided token Embedding weights. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end Args: old_embeddings (:obj:`tf.Variable`): Old embeddings to be resized. new_num_tokens (:obj:`int`, `optional`): New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens :obj:`tf.Variable`` module of the model without doing anything. Return: :obj:`tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if :obj:`new_num_tokens` is :obj:`None` """ old_embedding_dim = shape_list(old_embeddings)[1] init_range = getattr(self.config, "initializer_range", 0.02) embeddings_mask, current_embeddings = init_copy_embeddings(old_embeddings, new_num_tokens) new_embeddings = self.add_weight( name=old_embeddings.name.split(":")[0], shape=[new_num_tokens, old_embedding_dim], initializer=get_initializer(init_range), dtype=tf.float32, ) init_embeddings = tf.where(embeddings_mask, current_embeddings, new_embeddings.value()) new_embeddings.assign(init_embeddings) return new_embeddings
[docs] def prune_heads(self, heads_to_prune): """ Prunes heads of the base model. Arguments: heads_to_prune (:obj:`Dict[int, List[int]]`): Dictionary with keys being selected layer indices (:obj:`int`) and associated values being the list of heads to prune in said layer (list of :obj:`int`). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2. """ raise NotImplementedError
[docs] def save_pretrained(self, save_directory, saved_model=False, version=1, push_to_hub=False, **kwargs): """ Save a model and its configuration file to a directory, so that it can be re-loaded using the :func:`~transformers.TFPreTrainedModel.from_pretrained` class method. Arguments: save_directory (:obj:`str`): Directory to which to save. Will be created if it doesn't exist. saved_model (:obj:`bool`, `optional`, defaults to :obj:`False`): If the model has to be saved in saved model format as well or not. version (:obj:`int`, `optional`, defaults to 1): The version of the saved model. A saved model needs to be versioned in order to be properly loaded by TensorFlow Serving as detailed in the official documentation https://www.tensorflow.org/tfx/serving/serving_basic push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to push your model to the Hugging Face model hub after saving it. .. warning:: Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory instead. kwargs: Additional key word arguments passed along to the :meth:`~transformers.file_utils.PushToHubMixin.push_to_hub` method. """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return if push_to_hub: commit_message = kwargs.pop("commit_message", None) repo = self._create_or_get_repo(save_directory, **kwargs) os.makedirs(save_directory, exist_ok=True) if saved_model: saved_model_dir = os.path.join(save_directory, "saved_model", str(version)) self.save(saved_model_dir, include_optimizer=False, signatures=self.serving) logger.info(f"Saved model created in {saved_model_dir}") # Save configuration file self.config.architectures = [self.__class__.__name__[2:]] self.config.save_pretrained(save_directory) # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(save_directory, TF2_WEIGHTS_NAME) self.save_weights(output_model_file) logger.info(f"Model weights saved in {output_model_file}") if push_to_hub: url = self._push_to_hub(repo, commit_message=commit_message) logger.info(f"Model pushed to the hub in this commit: {url}")
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): r""" Instantiate a pretrained TF 2.0 model from a pre-trained model configuration. The warning `Weights from XXX not initialized from pretrained model` means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning `Weights from XXX not used in YYY` means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path (:obj:`str`, `optional`): Can be either: - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``. - A path to a `directory` containing model weights saved using :func:`~transformers.TFPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``. - A path or url to a `PyTorch state_dict save file` (e.g, ``./pt_model/pytorch_model.bin``). In this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards. - :obj:`None` if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``). model_args (sequence of positional arguments, `optional`): All remaining positional arguments will be passed to the underlying model's ``__init__`` method. config (:obj:`Union[PretrainedConfig, str]`, `optional`): Can be either: - an instance of a class derived from :class:`~transformers.PretrainedConfig`, - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`. Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when: - The model is a model provided by the library (loaded with the `model id` string of a pretrained model). - The model was saved using :func:`~transformers.TFPreTrainedModel.save_pretrained` and is reloaded by supplying the save directory. - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory. from_pt: (:obj:`bool`, `optional`, defaults to :obj:`False`): Load the model weights from a PyTorch state_dict save file (see docstring of ``pretrained_model_name_or_path`` argument). ignore_mismatched_sizes (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels). cache_dir (:obj:`str`, `optional`): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies: (:obj:`Dict[str, str], `optional`): A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`): Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to only look at local files (e.g., not try doanloading the model). use_auth_token (:obj:`str` or `bool`, `optional`): The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). revision(:obj:`str`, `optional`, defaults to :obj:`"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any identifier allowed by git. mirror(:obj:`str`, `optional`): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. kwargs (remaining dictionary of keyword arguments, `optional`): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., :obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or automatically loaded: - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function. .. note:: Passing :obj:`use_auth_token=True` is required when you want to use a private model. Examples:: >>> from transformers import BertConfig, TFBertModel >>> # Download model and configuration from huggingface.co and cache. >>> model = TFBertModel.from_pretrained('bert-base-uncased') >>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable). >>> model = TFBertModel.from_pretrained('./test/saved_model/') >>> # Update configuration during loading. >>> model = TFBertModel.from_pretrained('bert-base-uncased', output_attentions=True) >>> assert model.config.output_attentions == True >>> # Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable). >>> config = BertConfig.from_json_file('./pt_model/my_pt_model_config.json') >>> model = TFBertModel.from_pretrained('./pt_model/my_pytorch_model.bin', from_pt=True, config=config) """ config = kwargs.pop("config", None) cache_dir = kwargs.pop("cache_dir", None) from_pt = kwargs.pop("from_pt", False) ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) output_loading_info = kwargs.pop("output_loading_info", False) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) mirror = kwargs.pop("mirror", None) load_weight_prefix = kwargs.pop("load_weight_prefix", None) from_pipeline = kwargs.pop("_from_pipeline", None) from_auto_class = kwargs.pop("_from_auto", False) user_agent = {"file_type": "model", "framework": "tensorflow", "from_auto_class": from_auto_class} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: logger.info("Offline mode: forcing local_files_only=True") local_files_only = True # Load config if we don't provide a configuration if not isinstance(config, PretrainedConfig): config_path = config if config is not None else pretrained_model_name_or_path config, model_kwargs = cls.config_class.from_pretrained( config_path, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, _from_auto=from_auto_class, _from_pipeline=from_pipeline, **kwargs, ) else: model_kwargs = kwargs # Load model if pretrained_model_name_or_path is not None: if os.path.isdir(pretrained_model_name_or_path): if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)): # Load from a PyTorch checkpoint in priority if from_pt archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME) elif os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)): # Load from a TF 2.0 checkpoint archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME) else: raise EnvironmentError( f"Error no file named {[WEIGHTS_NAME, TF2_WEIGHTS_NAME]} found in directory " f"{pretrained_model_name_or_path} or `from_pt` set to False" ) elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): archive_file = pretrained_model_name_or_path elif os.path.isfile(pretrained_model_name_or_path + ".index"): archive_file = pretrained_model_name_or_path + ".index" else: archive_file = hf_bucket_url( pretrained_model_name_or_path, filename=(WEIGHTS_NAME if from_pt else TF2_WEIGHTS_NAME), revision=revision, mirror=mirror, ) try: # Load from URL or cache if already cached resolved_archive_file = cached_path( archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, ) except EnvironmentError as err: logger.error(err) msg = ( f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n" f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n" f" (make sure '{pretrained_model_name_or_path}' is not a path to a local directory with something else, in that case)\n\n" f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {TF2_WEIGHTS_NAME}, {WEIGHTS_NAME}.\n\n" ) raise EnvironmentError(msg) if resolved_archive_file == archive_file: logger.info(f"loading weights file {archive_file}") else: logger.info(f"loading weights file {archive_file} from cache at {resolved_archive_file}") else: resolved_archive_file = None config.name_or_path = pretrained_model_name_or_path # composed models, *e.g.* TFRag, require special treatment when it comes to loading # pre-trained weights. if cls._requires_load_weight_prefix and model_kwargs.get("name") is not None: model_kwargs["load_weight_prefix"] = load_weight_prefix + "/" + model_kwargs.get("name") # Instantiate model. model = cls(config, *model_args, **model_kwargs) if from_pt: from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model # Load from a PyTorch checkpoint return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True) # we might need to extend the variable scope for composite models if load_weight_prefix is not None: with tf.compat.v1.variable_scope(load_weight_prefix): model(model.dummy_inputs) # build the network with dummy inputs else: model(model.dummy_inputs) # build the network with dummy inputs assert os.path.isfile(resolved_archive_file), f"Error retrieving file {resolved_archive_file}" # 'by_name' allow us to do transfer learning by skipping/adding layers # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357 try: missing_keys, unexpected_keys, mismatched_keys = load_tf_weights( model, resolved_archive_file, ignore_mismatched_sizes=ignore_mismatched_sizes, _prefix=load_weight_prefix, ) except OSError as e: try: with open(resolved_archive_file) as f: if f.read().startswith("version"): raise OSError( "You seem to have cloned a repository without having git-lfs installed. Please install " "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder " "you cloned." ) else: raise ValueError from e except (UnicodeDecodeError, ValueError): raise OSError( "Unable to load weights from h5 file. " "If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. " ) model(model.dummy_inputs) # Make sure restore ops are run if cls._keys_to_ignore_on_load_missing is not None: for pat in cls._keys_to_ignore_on_load_missing: missing_keys = [k for k in missing_keys if re.search(pat, k) is None] if cls._keys_to_ignore_on_load_unexpected is not None: for pat in cls._keys_to_ignore_on_load_unexpected: unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] if len(unexpected_keys) > 0: logger.warning( f"Some layers from the model checkpoint at {pretrained_model_name_or_path} were not used when " f"initializing {model.__class__.__name__}: {unexpected_keys}\n" f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task " f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n" f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect " f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)." ) else: logger.warning(f"All model checkpoint layers were used when initializing {model.__class__.__name__}.\n") if len(missing_keys) > 0: logger.warning( f"Some layers of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} " f"and are newly initialized: {missing_keys}\n" f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference." ) elif len(mismatched_keys) == 0: logger.warning( f"All the layers of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n" f"If your task is similar to the task the model of the checkpoint was trained on, " f"you can already use {model.__class__.__name__} for predictions without further training." ) if len(mismatched_keys) > 0: mismatched_warning = "\n".join( [ f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" for key, shape1, shape2 in mismatched_keys ] ) logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} " f"and are newly initialized because the shapes did not match:\n{mismatched_warning}\n" f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference." ) if output_loading_info: loading_info = { "missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "mismatched_keys": mismatched_keys, } return model, loading_info return model
# To update the docstring, we need to copy the method, otherwise we change the original docstring. TFPreTrainedModel.push_to_hub = copy_func(TFPreTrainedModel.push_to_hub) TFPreTrainedModel.push_to_hub.__doc__ = TFPreTrainedModel.push_to_hub.__doc__.format( object="model", object_class="TFAutoModel", object_files="model checkpoint" )
[docs]class TFConv1D(tf.keras.layers.Layer): """ 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). Basically works like a linear layer but the weights are transposed. Args: nf (:obj:`int`): The number of output features. nx (:obj:`int`): The number of input features. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation to use to initialize the weights. kwargs: Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`. """ def __init__(self, nf, nx, initializer_range=0.02, **kwargs): super().__init__(**kwargs) self.nf = nf self.nx = nx self.initializer_range = initializer_range def build(self, input_shape): self.weight = self.add_weight( "weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range) ) self.bias = self.add_weight("bias", shape=[1, self.nf], initializer=tf.zeros_initializer()) def call(self, x): bz, sl = shape_list(x)[:2] x = tf.reshape(x, [-1, self.nx]) x = tf.matmul(x, self.weight) + self.bias x = tf.reshape(x, [bz, sl, self.nf]) return x
[docs]class TFSharedEmbeddings(tf.keras.layers.Layer): r""" Construct shared token embeddings. The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language modeling. Args: vocab_size (:obj:`int`): The size of the vocabulary, e.g., the number of unique tokens. hidden_size (:obj:`int`): The size of the embedding vectors. initializer_range (:obj:`float`, `optional`): The standard deviation to use when initializing the weights. If no value is provided, it will default to :math:`1/\sqrt{hidden\_size}`. kwargs: Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`. """ def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs): super().__init__(**kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.initializer_range = hidden_size ** -0.5 if initializer_range is None else initializer_range def build(self, input_shape): """ Build shared token embedding layer Shared weights logic adapted from https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24 """ self.weight = self.add_weight( "weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range) ) super().build(input_shape) def get_config(self): config = { "vocab_size": self.vocab_size, "hidden_size": self.hidden_size, "initializer_range": self.initializer_range, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
[docs] def call(self, inputs: tf.Tensor, mode: str = "embedding") -> tf.Tensor: """ Get token embeddings of inputs or decode final hidden state. Args: inputs (:obj:`tf.Tensor`): In embedding mode, should be an int64 tensor with shape :obj:`[batch_size, length]`. In linear mode, should be a float tensor with shape :obj:`[batch_size, length, hidden_size]`. mode (:obj:`str`, defaults to :obj:`"embedding"`): A valid value is either :obj:`"embedding"` or :obj:`"linear"`, the first one indicates that the layer should be used as an embedding layer, the second one that the layer should be used as a linear decoder. Returns: :obj:`tf.Tensor`: In embedding mode, the output is a float32 embedding tensor, with shape :obj:`[batch_size, length, embedding_size]`. In linear mode, the output is a float32 with shape :obj:`[batch_size, length, vocab_size]`. Raises: ValueError: if :obj:`mode` is not valid. Shared weights logic is adapted from `here <https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24>`__. """ if mode == "embedding": return self._embedding(inputs) elif mode == "linear": return self._linear(inputs) else: raise ValueError(f"mode {mode} is not valid.")
def _embedding(self, input_ids): """Applies embedding based on inputs tensor.""" return tf.gather(self.weight, input_ids) def _linear(self, inputs): """ Computes logits by running inputs through a linear layer. Args: inputs: A float32 tensor with shape [..., hidden_size] Returns: float32 tensor with shape [..., vocab_size]. """ first_dims = shape_list(inputs)[:-1] x = tf.reshape(inputs, [-1, self.hidden_size]) logits = tf.matmul(x, self.weight, transpose_b=True) return tf.reshape(logits, first_dims + [self.vocab_size])
[docs]class TFSequenceSummary(tf.keras.layers.Layer): """ Compute a single vector summary of a sequence hidden states. Args: config (:class:`~transformers.PretrainedConfig`): The config used by the model. Relevant arguments in the config class of the model are (refer to the actual config class of your model for the default values it uses): - **summary_type** (:obj:`str`) -- The method to use to make this summary. Accepted values are: - :obj:`"last"` -- Take the last token hidden state (like XLNet) - :obj:`"first"` -- Take the first token hidden state (like Bert) - :obj:`"mean"` -- Take the mean of all tokens hidden states - :obj:`"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2) - :obj:`"attn"` -- Not implemented now, use multi-head attention - **summary_use_proj** (:obj:`bool`) -- Add a projection after the vector extraction. - **summary_proj_to_labels** (:obj:`bool`) -- If :obj:`True`, the projection outputs to :obj:`config.num_labels` classes (otherwise to :obj:`config.hidden_size`). - **summary_activation** (:obj:`Optional[str]`) -- Set to :obj:`"tanh"` to add a tanh activation to the output, another string or :obj:`None` will add no activation. - **summary_first_dropout** (:obj:`float`) -- Optional dropout probability before the projection and activation. - **summary_last_dropout** (:obj:`float`)-- Optional dropout probability after the projection and activation. initializer_range (:obj:`float`, defaults to 0.02): The standard deviation to use to initialize the weights. kwargs: Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`. """ def __init__(self, config: PretrainedConfig, initializer_range: float = 0.02, **kwargs): super().__init__(**kwargs) self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last" if self.summary_type == "attn": # We should use a standard multi-head attention module with absolute positional embedding for that. # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 # We can probably just use the multi-head attention module of PyTorch >=1.1.0 raise NotImplementedError self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj if self.has_summary: if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0: num_classes = config.num_labels else: num_classes = config.hidden_size self.summary = tf.keras.layers.Dense( num_classes, kernel_initializer=get_initializer(initializer_range), name="summary" ) self.has_activation = hasattr(config, "summary_activation") and config.summary_activation == "tanh" if self.has_activation: self.activation = tf.keras.activations.tanh self.has_first_dropout = hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0 if self.has_first_dropout: self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout) self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0 if self.has_last_dropout: self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout) def call(self, inputs, cls_index=None, training=False): if not isinstance(inputs, (dict, tuple, list)): hidden_states = inputs elif isinstance(inputs, (tuple, list)): hidden_states = inputs[0] cls_index = inputs[1] if len(inputs) > 1 else None assert len(inputs) <= 2, "Too many inputs." else: hidden_states = inputs.get("hidden_states") cls_index = inputs.get("cls_index", None) if self.summary_type == "last": output = hidden_states[:, -1] elif self.summary_type == "first": output = hidden_states[:, 0] elif self.summary_type == "mean": output = tf.reduce_mean(hidden_states, axis=1) elif self.summary_type == "cls_index": hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims] if cls_index is None: cls_index = tf.fill( hidden_shape[:-2], hidden_shape[-2] - 1 ) # A tensor full of shape [batch] or [batch, num choices] full of sequence length cls_shape = shape_list(cls_index) if len(cls_shape) <= len(hidden_shape) - 2: cls_index = tf.expand_dims(cls_index, axis=-1) # else: # cls_index = cls_index[..., tf.newaxis] # cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),)) # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2) output = tf.squeeze( output, axis=len(hidden_shape) - 2 ) # shape of output: (batch, num choices, hidden_size) elif self.summary_type == "attn": raise NotImplementedError if self.has_first_dropout: output = self.first_dropout(output, training=training) if self.has_summary: output = self.summary(output) if self.has_activation: output = self.activation(output) if self.has_last_dropout: output = self.last_dropout(output, training=training) return output
[docs]def shape_list(tensor: tf.Tensor) -> List[int]: """ Deal with dynamic shape in tensorflow cleanly. Args: tensor (:obj:`tf.Tensor`): The tensor we want the shape of. Returns: :obj:`List[int]`: The shape of the tensor as a list. """ dynamic = tf.shape(tensor) if tensor.shape == tf.TensorShape(None): return dynamic static = tensor.shape.as_list() return [dynamic[i] if s is None else s for i, s in enumerate(static)]
[docs]def get_initializer(initializer_range: float = 0.02) -> tf.initializers.TruncatedNormal: """ Creates a :obj:`tf.initializers.TruncatedNormal` with the given range. Args: initializer_range (`float`, defaults to 0.02): Standard deviation of the initializer range. Returns: :obj:`tf.initializers.TruncatedNormal`: The truncated normal initializer. """ return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
class TFWrappedEmbeddings: """ this class wraps a the TFSharedEmbeddingTokens layer into a python 'no-keras-layer' class to avoid problem with weight restoring. Also it makes sure that the layer is called from the correct scope to avoid problem with saving/storing the correct weights """ def __init__(self, layer, abs_scope_name=None): self._layer = layer self._abs_scope_name = abs_scope_name def call(self, inputs, mode="embedding"): if self._abs_scope_name is None: return self._layer.call(inputs, mode) # if an abs scope name is given to the embedding variable, call variable from absolute scope with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name: with tf.name_scope(abs_scope_name.original_name_scope): return self._layer.call(inputs, mode) def __call__(self, inputs, mode="embedding"): if self._abs_scope_name is None: return self._layer(inputs, mode) # if an abs scope name is given to the embedding variable, call variable from absolute scope with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name: with tf.name_scope(abs_scope_name.original_name_scope): return self._layer(inputs, mode)